neural_network.optimizers.nesterov_accelerated_sgd
==================================================

.. py:module:: neural_network.optimizers.nesterov_accelerated_sgd

.. autoapi-nested-parse::

   Nesterov Accelerated Gradient (NAG) Optimizer

   Implements Nesterov momentum for neural network training using NumPy.
   NAG looks ahead and computes gradients at the anticipated position.

   Reference: https://cs231n.github.io/neural-networks-3/#sgd
   Author: Adhithya Laxman Ravi Shankar Geetha
   Date: 2025.10.21



Attributes
----------

.. autoapisummary::

   neural_network.optimizers.nesterov_accelerated_sgd.optimizer


Classes
-------

.. autoapisummary::

   neural_network.optimizers.nesterov_accelerated_sgd.NesterovAcceleratedGradient


Module Contents
---------------

.. py:class:: NesterovAcceleratedGradient(learning_rate: float = 0.01, momentum: float = 0.9)

   Nesterov Accelerated Gradient (NAG) optimizer.

   Updates parameters using Nesterov momentum:
       velocity = momentum * velocity - learning_rate * gradient_at_lookahead
       param = param + velocity


   .. py:method:: update(param_id: int, params: numpy.ndarray, gradients: numpy.ndarray) -> numpy.ndarray

      Update parameters using NAG.

      Args:
          param_id (int): Unique identifier for parameter group.
          params (np.ndarray): Current parameters.
          gradients (np.ndarray): Gradients at lookahead position.

      Returns:
          np.ndarray: Updated parameters.

      >>> optimizer = NesterovAcceleratedGradient(learning_rate=0.1, momentum=0.9)
      >>> params = np.array([1.0, 2.0])
      >>> grads = np.array([0.1, 0.2])
      >>> updated = optimizer.update(0, params, grads)
      >>> updated.shape
      (2,)



   .. py:attribute:: learning_rate
      :value: 0.01



   .. py:attribute:: momentum
      :value: 0.9



   .. py:attribute:: velocity
      :type:  dict[int, numpy.ndarray]


.. py:data:: optimizer